发明名称 |
BIG DATA PROCESSING METHOD BASED ON DEEP LEARNING MODEL SATISFYING K-DEGREE SPARSE CONSTRAINT |
摘要 |
Proposed is a big data processing method based on a deep learning model satisfying K-degree sparse constraints. The method comprises: step 1), constructing a deep learning model satisfying K-degree sparse constraints using an un-marked training sample via a gradient pruning method, wherein the K-degree sparse constraints comprise a node K-degree sparse constraint and a level K-degree sparse constraint; step 2), inputting an updated training sample into the deep learning model satisfying the K-degree sparse constraints, and optimizing a weight parameter of each layer of the model, so as to obtain an optimized deep learning model satisfying the K-degree sparse constraint; and step 3), inputting big data to be processed into the optimized deep learning model satisfying the K-degree sparse constraints for processing, and finally outputting a processing result. The method in the present invention can reduce the difficulty of big data processing and increase the speed of big data processing. |
申请公布号 |
WO2016145676(A1) |
申请公布日期 |
2016.09.22 |
申请号 |
WO2015CN75473 |
申请日期 |
2015.03.31 |
申请人 |
INSTITUTE OF ACOUSTICS,CHINESE ACADEMY OF SCIENCES;SHANGHAI 3NTV NETWORK TECHNOLOGY CO. LTD. |
发明人 |
SHENG, Yiqiang;WANG, Jinlin;DENG, Haojiang;YOU, Jiali |
分类号 |
G06N5/00 |
主分类号 |
G06N5/00 |
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